Alle Publikationen
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2008
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(2008) : Measuring human-robot team effectiveness to determine an appropriate autonomy level: 2008 IEEE International Conference on Robotics and Automation: 2008 IEEE International Conference on Robotics and Automation: Pasadena, CA, USA: IEEE, S. 2146-2151
DOI: https://doi.org/10.1109/ROBOT.2008.4543524 Abstract: This paper proposes a methodology to measure the effectiveness of a human-robot team as part of an adjustable autonomy system. The effectiveness measure is aimed at determining an appropriate autonomy level prior to the system’s deployment. Two competing goals need to be traded off: maximising robot performance while minimising the amount of human input. The relative importance of the two goals depend on the mission priorities and constraints which are taken into account. The proposed methodology is applied to a human-robot communication system developed for task- oriented information exchange. The robot uses a decision- theoretic framework to act autonomously and to decide when to request input from human operators. The latter is achieved by computing the value-of-information an operator is able to provide which is compared to the cost of obtaining the information. For our system, the cost parameter represents the autonomy level to be determined. We demonstrate how an appropriate autonomy level can be found experimentally using a navigation task. In our experiment, the robot navigates through a set of simulated worlds with human input being generated by a software component. The results are used to find appropriate autonomy levels for three example missions and a subsequent user study.
Keywords: Angemessen(heit) (von Technik), Anthropometry, Automation, Cognitive robotics, Communication systems, Costs, decision-theoretic framework, Human robot interaction, human-robot communication system, ieee xplore, man-machine systems, Measurement, Navigation, Robot sensing systems, Robotics, Robots, safety, software component, task-oriented information exchange 2005
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(2005): Active affective State detection and user assistance with dynamic bayesian networks. In: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 35 (1), S. 93-105. DOI: 10.1109/TSMCA.2004.838454
DOI: https://doi.org/10.1109/TSMCA.2004.838454 Abstract: With the rapid development of pervasive and ubiquitous computing applications, intelligent user-assistance systems face challenges of ambiguous, uncertain, and multimodal sensory observations, user’s changing state, and various constraints on available resources and costs in making decisions. We introduce a new probabilistic framework based on the dynamic Bayesian networks (DBNs) to dynamically model and recognize user’s affective states and to provide the appropriate assistance in order to keep user in a productive state. We incorporate an active sensing mechanism into the DBN framework to perform purposive and sufficing information integration in order to infer user’s affective state and to provide correct assistance in a timely and efficient manner. Experiments involving both synthetic and real data demonstrate the feasibility of the proposed framework as well as the effectiveness of the proposed active sensing strategy.
Keywords: active affective state detection, active fusion, active sensing mechanism, affective state detection, Angemessen(heit) (von Technik), Bayesian methods, Bayesian networks (BNs), belief networks, Context modeling, Costs, dynamic Bayesian networks, Face detection, ieee xplore, information integration, Information theory, Intelligent networks, Intelligent sensors, Intelligent Systems, intelligent user assistance system, probabilistic framework, sensor fusion, Systems engineering, theory, Ubiquitous computing, user assistance, user interfaces 2004
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(2004) : Toward an actualization of social intelligence in human and robot collaborative systems: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 4: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566): Sendai, Japan: IEEE
DOI: https://doi.org/10.1109/IROS.2004.1389916 Abstract: As robot technology is evolving and creating a social community between humans and robots, it is necessary to research and develop a new type of intelligence, which we refer to as "social intelligence”. Social intelligence enables natural and socially appropriate interactions. Its importance is gaining a growing interest among not just the human-computer interaction researchers but also robot technology researchers and developers. This article discusses the definition, importance, and benefits of social intelligence in human and robot collaborative systems. The virtual social environment is employed to implement an experimental social intelligence system because of its low cost and high flexibility. Software robots (i.e. agents) with the social intelligence model have been implemented by featuring an emotion model and a personality model under the virtual environment. The social intelligence model that handles affective responses is based on the theories of personality, emotion, and human-media interaction such as cognitive appraisal theory and media equation. The experiment was conducted with the virtual learning collaborative system to examine the effect of the social intelligence model in the collaborative system. The data showed that the users had more positive impressions about the usefulness and the application and learning experience when the cooperative agent displayed some social responses with personality and emotions. It should be noted here that the cooperative agent did not provide any explicit assistance for the human user such as giving clues and showing answers, and yet the user’s evaluation on the usefulness of the learning system was influenced by the social agent. The data also suggested that the cooperative agent contributed to the effectiveness of the learning system.
Keywords: APPRAISAL, Artificial intelligence, Cognitive robotics, Collaboration, Costs, groupware, human collaborative system, Human Computer Interaction, Human robot interaction, Humanoid Robots, ieee xplore, intelligent agent, intelligent robots, Intelligent Systems, Künstliche Intelligenz, learning system, learning systems, robot collaborative system, robot technology, Social intelligence, Software agents, software robot, Virtual environment
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